DocumentCode :
3393134
Title :
On damage monitoring in historical buildings via Neural Networks
Author :
Carnimeo, Leonarda ; Foti, Dora ; Vacca, Vitantonio
Author_Institution :
Dept. of Electr. & Inf. Eng., Tech. Univ. of Bari, Bari, Italy
fYear :
2015
fDate :
9-10 July 2015
Firstpage :
157
Lastpage :
161
Abstract :
It is well known that ancient buildings suffer a high vulnerability to hazards, which may induce unpredictable damages. For this purpose, a main objective to be pursued concerns with the development of techniques for monitoring historical buildings and immediately alerting in case of early vulnerability warnings. This paper proposes a noninvasive Neural Network-based (NN-based) approach for Monitoring heritage buildings providing alerts in risk events. More in detail, a neural approach is suggested with the aim of predicting early warnings of risk events by detecting time novelties in images of historical evidences.
Keywords :
buildings (structures); condition monitoring; hazards; history; neural nets; risk analysis; structural engineering computing; NN; ancient buildings; damage monitoring; early vulnerability warnings; hazard vulnerability; heritage buildings; historical buildings; historical evidences; noninvasive neural network-based approach; risk events; Artificial neural networks; Biological neural networks; Buildings; Cameras; Monitoring; Neurons; Training; Risk prevention; historical buildings; image processing techniques; neural networks; sensor-based monitoring systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Environmental, Energy and Structural Monitoring Systems (EESMS), 2015 IEEE Workshop on
Conference_Location :
Trento
Print_ISBN :
978-1-4799-8214-1
Type :
conf
DOI :
10.1109/EESMS.2015.7175870
Filename :
7175870
Link To Document :
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